research-article

Understanding Urban Dynamics via State-sharing Hidden Markov Model

Authors:

Yong LiAuthors Info & Claims

WWW '19: The World Wide Web Conference

Pages 3363 - 3369

https://doi.org/10.1145/3308558.3313453

Published: 13 May 2019 Publication History

Abstract

Modeling people's activities in the urban space is a crucial socio-economic task but extremely challenging due to the deficiency of suitable methods. To model the temporal dynamics of human activities concisely and specifically, we present State-sharing Hidden Markov Model (SSHMM). First, it extracts the urban states from the whole city, which captures the volume of population flows as well as the frequency of each type of Point of Interests (PoIs) visited. Second, it characterizes the urban dynamics of each urban region as the state transition on the shared-states, which reveals distinct daily rhythms of urban activities. We evaluate our method via a large-scale real-life mobility dataset and results demonstrate that SSHMM learns semantics-rich urban dynamics, which are highly correlated with the functions of the region. Besides, it recovers the urban dynamics in different time slots with an error of 0.0793, which outperforms the general HMM by 54.2%.

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Cited By

Hamdi AShaban KErradi AMohamed ARumi SSalim F(2022)Spatiotemporal data mining: a survey on challenges and open problemsArtificial Intelligence Review10.1007/s10462-021-09994-y55:2(1441-1488)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s10462-021-09994-y
Li JPedrycz WWang XLiu P(2022)A Hidden Markov Model-based fuzzy modeling of multivariate time seriesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07623-627:2(837-854)Online publication date: 18-Nov-2022
https://dl.acm.org/doi/10.1007/s00500-022-07623-6
Xia TLin JLi YFeng JHui PSun FGuo DJin D(2021)3DGCN: 3-Dimensional Dynamic Graph Convolutional Network for Citywide Crowd Flow PredictionACM Transactions on Knowledge Discovery from Data10.1145/345139415:6(1-21)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1145/3451394
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cover image ACM Other conferences

WWW '19: The World Wide Web Conference

May 2019

3620 pages

ISBN:9781450366748

DOI:10.1145/3308558

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 May 2019

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WWW '19: The Web Conference

May 13 - 17, 2019

CA, San Francisco, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Hamdi AShaban KErradi AMohamed ARumi SSalim F(2022)Spatiotemporal data mining: a survey on challenges and open problemsArtificial Intelligence Review10.1007/s10462-021-09994-y55:2(1441-1488)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s10462-021-09994-y
Li JPedrycz WWang XLiu P(2022)A Hidden Markov Model-based fuzzy modeling of multivariate time seriesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07623-627:2(837-854)Online publication date: 18-Nov-2022
https://dl.acm.org/doi/10.1007/s00500-022-07623-6
Xia TLin JLi YFeng JHui PSun FGuo DJin D(2021)3DGCN: 3-Dimensional Dynamic Graph Convolutional Network for Citywide Crowd Flow PredictionACM Transactions on Knowledge Discovery from Data10.1145/345139415:6(1-21)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1145/3451394
Maag BFeuerriegel SKraus MSaar-Tsechansky MZüger TGhassemi MNaumann TPierson E(2021)Modeling longitudinal dynamics of comorbiditiesProceedings of the Conference on Health, Inference, and Learning10.1145/3450439.3451871(222-235)Online publication date: 8-Apr-2021
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Xia TLi YYu YXu FLiao QJin D(2021)Understanding Urban Dynamics via State-Sharing Hidden Markov ModelIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.296843233:10(3468-3481)Online publication date: 1-Oct-2021
https://doi.org/10.1109/TKDE.2020.2968432
Nakamura RSano HInagaki AOsawa RTakagi TMunemasa I(2021)Dynamic Topic-Enhanced Memory Networks: Time-series Behavior Prediction based on Changing Intrinsic Consciousnesses2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR51284.2021.00035(185-191)Online publication date: Sep-2021
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Osawa RSuekane KNakamura RInagaki ATakagi TMunemasa I(2021)Predicting Human Behavior with Transformer Considering the Mutual Relationship between Categories and Regions2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR51284.2021.00029(144-150)Online publication date: Sep-2021
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Yu YXia TWang HFeng JLi Y(2020)Semantic-aware Spatio-temporal App Usage Representation via Graph Convolutional NetworkProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34118174:3(1-24)Online publication date: 4-Sep-2020
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